Design of a Hybrid Recommendation Algorithm based on Multi-objective Collaborative Filtering for Massive Cloud Data

Abstract

The current recommendation technology has some problems, such as lack of timeliness, the contradiction between recommendation diversity and accuracy. In order to solve the problem of lack of timeliness, the time factor is introduced when constructing the self-preference model. The cold start problem in the collaborative filtering algorithm is solved by the hybrid similarity calculation method, and the potential preference model is constructed. The two are fused to obtain a hybrid recommendation algorithm to improve the recommendation performance of the algorithm. For the problem of multi-objective contradiction, the NNIA algorithm is used to further optimize the candidate results of mixed recommendation, and the final recommendation list is obtained. Through verification experiments, the results show that the recall rate and accuracy of the fused preference model are better than those of the non-fused model, and the accuracy is 9.57% and 8.23% higher than that of SPM and PPM, and the recall rate is 9.97% and 7.65% higher, respectively. CBCF-NNIA algorithm has high accuracy and diversity of recommendation, and can provide users with rich and diverse text content to meet their own needs.